{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import arrow\n",
    "import json\n",
    "\n",
    "con = sqlite3.connect('./data/time_series.db')\n",
    "cur = con.execute(\"select * from tsdb_hlct;\")\n",
    "\n",
    "data = []\n",
    "for each in cur:\n",
    "#     print(arrow.get(each[0]).to('local'))\n",
    "#     print(each[2])\n",
    "    data.append({'time':arrow.get(each[0]).to('local').format('YYYY-MM-DD HH:mm:ss'),'rate':each[2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2017-07-09 15:27:44\n",
       "1     2017-07-09 15:28:12\n",
       "2     2017-07-09 15:28:33\n",
       "3     2017-07-09 15:28:55\n",
       "4     2017-07-09 15:29:17\n",
       "5     2017-07-09 15:29:39\n",
       "6     2017-07-09 15:30:00\n",
       "7     2017-07-09 15:30:22\n",
       "8     2017-07-09 15:30:44\n",
       "9     2017-07-09 15:31:06\n",
       "10    2017-07-09 15:31:27\n",
       "11    2017-07-09 15:31:49\n",
       "12    2017-07-09 15:32:11\n",
       "13    2017-07-09 15:32:33\n",
       "14    2017-07-09 15:32:55\n",
       "15    2017-07-09 15:33:16\n",
       "16    2017-07-09 15:33:38\n",
       "17    2017-07-09 15:34:00\n",
       "18    2017-07-09 15:34:21\n",
       "19    2017-07-09 15:34:43\n",
       "20    2017-07-09 15:35:05\n",
       "21    2017-07-09 15:35:27\n",
       "22    2017-07-09 15:35:48\n",
       "23    2017-07-09 15:36:10\n",
       "24    2017-07-09 15:36:32\n",
       "25    2017-07-09 15:36:54\n",
       "26    2017-07-09 15:37:16\n",
       "27    2017-07-09 15:37:37\n",
       "28    2017-07-09 15:37:59\n",
       "29    2017-07-09 15:38:21\n",
       "              ...        \n",
       "141   2017-07-09 16:54:12\n",
       "142   2017-07-09 16:54:33\n",
       "143   2017-07-09 16:54:56\n",
       "144   2017-07-09 16:55:17\n",
       "145   2017-07-09 16:55:39\n",
       "146   2017-07-09 16:56:01\n",
       "147   2017-07-09 16:56:22\n",
       "148   2017-07-09 16:56:44\n",
       "149   2017-07-09 16:57:06\n",
       "150   2017-07-09 16:57:28\n",
       "151   2017-07-09 16:57:50\n",
       "152   2017-07-09 16:58:11\n",
       "153   2017-07-09 16:58:33\n",
       "154   2017-07-09 16:58:55\n",
       "155   2017-07-09 16:59:17\n",
       "156   2017-07-09 16:59:38\n",
       "157   2017-07-09 17:00:00\n",
       "158   2017-07-09 17:00:22\n",
       "159   2017-07-09 17:00:43\n",
       "160   2017-07-09 17:01:05\n",
       "161   2017-07-09 17:01:27\n",
       "162   2017-07-09 17:01:49\n",
       "163   2017-07-09 17:02:11\n",
       "164   2017-07-09 17:02:32\n",
       "165   2017-07-09 17:02:55\n",
       "166   2017-07-09 17:03:17\n",
       "167   2017-07-09 17:03:38\n",
       "168   2017-07-09 17:04:00\n",
       "169   2017-07-09 17:04:22\n",
       "170   2017-07-09 17:04:43\n",
       "Name: time, Length: 171, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "\n",
    "df = pd.read_json(json.dumps(data))\n",
    "df.time = pd.to_datetime(df.time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GYw1wrJlNMDMjqMVYGd/BzKaH2zCzowm6fzbEdnkf6k4RERFpCU0ZpeLui8zsGuABIA88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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d66550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import arrow\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from matplotlib.dates import DateFormatter\n",
    "font = {'family' : 'serif',  \n",
    "        'color'  : 'darkred',  \n",
    "        'weight' : 'normal',  \n",
    "        'size'   : 16,  \n",
    "        }  \n",
    "\n",
    "arrow.now()\n",
    "\n",
    "fig = plt.figure(111, figsize=(8,5))  # figure 定义一张图片, figsize决定图片的大小 \n",
    "ax =  fig.add_axes([0.12, 0.25, 0.82, 0.8])  # 在图片上定义一块绘图区域，可以通过 fig.gca() 获得\n",
    "ax.plot(df.time,df.rate)\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "\n",
    "# ax.xaxis.set_major_formatter(dates.DateFormatter('%H:%M:%S'))#设置时间标签显示格式\n",
    "# plt.xticks(pd.date_range(df.time[0],df.time[-1],freq='1min'))\n",
    "\n",
    "# plt.plot(df.time,df.rate)\n",
    "plt.xlim(arrow.now().replace(hours=+4).datetime,)\n",
    "\n",
    "xaxis = ax.xaxis  # 获取绘图区域的x轴\n",
    "ax.set_ylabel('%/year',fontdict=font)\n",
    "xaxis.set_major_locator(MultipleLocator(1/24/60*25))  # x轴的坐标间隔设置为1\n",
    "xaxis.set_major_formatter(DateFormatter('%H:%M'))  # 格式化日期\n",
    "# fig.autofmt_xdate()  # 自动格式化日期，防止其溢出绘图区域\n",
    "plt.title(\"Interest rates in the last 4 hours\",fontdict=font)\n",
    "plt.grid() \n",
    "plt.show()\n",
    "fig.savefig('temp.png')\n",
    "\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# ax.plot_date(df.time, df.rate, 'v-')\n",
    "# ax.xaxis.set_minor_locator(dates.MinuteLocator())\n",
    "# # ax.xaxis.set_minor_formatter(dates.AutoDateFormatter(dates.AutoDateLocator()))\n",
    "# # ax.xaxis.grid(True, which=\"minor\")\n",
    "# # ax.yaxis.grid()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118eb78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1190077f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "\n",
    "def update_line(num, data, line):\n",
    "    line.set_data(data[..., :num])\n",
    "    return line,\n",
    "\n",
    "fig1 = plt.figure()\n",
    "\n",
    "data = np.random.rand(2, 25)\n",
    "l, = plt.plot([], [], 'r-')\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('x')\n",
    "plt.title('test')\n",
    "line_ani = animation.FuncAnimation(fig1, update_line, 25, fargs=(data, l),\n",
    "                                   interval=50, blit=True)\n",
    "\n",
    "# To save the animation, use the command: line_ani.save('lines.mp4')\n",
    "\n",
    "fig2 = plt.figure()\n",
    "\n",
    "x = np.arange(-9, 10)\n",
    "y = np.arange(-9, 10).reshape(-1, 1)\n",
    "base = np.hypot(x, y)\n",
    "ims = []\n",
    "for add in np.arange(15):\n",
    "    ims.append((plt.pcolor(x, y, base + add, norm=plt.Normalize(0, 30)),))\n",
    "\n",
    "im_ani = animation.ArtistAnimation(fig2, ims, interval=50, repeat_delay=3000,\n",
    "                                   blit=True)\n",
    "# To save this second animation with some metadata, use the following command:\n",
    "# im_ani.save('im.mp4', metadata={'artist':'Guido'})\n",
    "\n",
    "plt.show()"
   ]
  }
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